sar-denoising

说明：  Kirsch算子边缘检测代码，计算了八个方向的梯度值(Kirsch operator edge detection code)

说明：  采用大津阈值法检测车道线，效果还可以，有需要的可以看看(Using the Dajin threshold method to detect the lane line, the effect is still good, you can see if you need it.)

图像DFT，C语言编写的代码，附带几个另外的处理，(Image DFT, C language code, with some additional processing,)

今年刚更新的最新的工具箱SaliencyToolbox2.3.zip，做图像处理视觉注意力机制相关研究必备的工具。(This year has just updated the latest toolbox SaliencyToolbox2.3.zip, do image processing of visual attention mechanisms related research is an essential tool.)

) 使用分块的主成分分析方法(PCA)对人脸图像进行压缩编码。针对PCA方法计算量大的缺点,首先把问题转化成奇异值分解(SVD)问题,然后设计了特征空间的更新算法,通过递推,简化每一步计算的计算量,达到了实时编码的要求。 4) 在Windows平台下基于Video for Windows(VFW)接口开发了人脸视频图像编码和解码的实验系统,该系统实现了图像采集、图像显示、编码、解码等功能。() The use of sub-blocks of principal component analysis (PCA) on the human face image coding. PCA method for large defects, first of all the issues into a singular value decomposition (SVD) problem, and then design a feature space of the update algorithm, recursive, simplifying the calculation of the amount calculated at each step to achieve real-time encoding requirements. 4) In the Windows platform based on Video for Windows (VFW) interfaces, the human face image coding and decoding video experimental system, the system achieved image acquisition, image display, encoding, decoding functions.)

实现bmp图像读入和写出，彩色图像灰度化，直方图均衡化(Implement bmp image read and write, color image graying, histogram equalization)

二维图像视觉显著性检测，效果非常好。California Institute of Technology的 Jonathan Harel写的，很经典。(Two-dimensional image saliency detection, the effect was very good. California Institute of Technology' s Jonathan Harel written, very classic.)

高分辨率及新模式SAR的成像算法，含有圆轨迹快速成像和压缩感知等内容(High resolution and the new mode of SAR imaging algorithm, containing circular trajectory fast imaging and compressed sensing and other content)

说明：  二进小波变换的图像增强,matlab代码(Dyadic wavelet transform image enhancement, matlab code)

说明：  图像盲复原，图像非盲复原，利用低秩分解进行图像盲复原处理，RPCA模型算法，GoDec模型算法。(Blind image restoration, non-blind image restoration, low rank decomposition)